




'''simple docstring'''







import argparse
import os

import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints

from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder


lowerCAmelCase_     						=				'''base_with_context'''







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     nn.Parameter(torch.FloatTensor(weights['token_embedder']['embedding']						)						)
   A__       =     nn.Parameter(
       torch.FloatTensor(weights['Embed_0']['embedding']						)      ,requires_grad=UpperCAmelCase						)
   for lyr_num, lyr in enumerate(model.encoders						):
      A__       =     weights[F"""layers_{lyr_num}"""]
      A__       =     nn.Parameter(
          torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale']						)						)

      A__       =     ly_weight['attention']
      A__       =     nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T						)						)
      A__       =     nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T						)						)
      A__       =     nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T						)						)
      A__       =     nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T						)						)

      A__       =     nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']						)						)

      A__       =     nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T						)						)
      A__       =     nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T						)						)
      A__       =     nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T						)						)

   A__       =     nn.Parameter(torch.FloatTensor(weights['encoder_norm']['scale']						)						)
   return model







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     nn.Parameter(torch.FloatTensor(weights['input_proj']['kernel'].T						)						)

   A__       =     nn.Parameter(
       torch.FloatTensor(weights['Embed_0']['embedding']						)      ,requires_grad=UpperCAmelCase						)

   for lyr_num, lyr in enumerate(model.encoders						):
      A__       =     weights[F"""layers_{lyr_num}"""]
      A__       =     ly_weight['attention']

      A__       =     nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T						)						)
      A__       =     nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T						)						)
      A__       =     nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T						)						)
      A__       =     nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T						)						)
      A__       =     nn.Parameter(
          torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale']						)						)

      A__       =     nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T						)						)
      A__       =     nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T						)						)
      A__       =     nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T						)						)
      A__       =     nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']						)						)

   A__       =     nn.Parameter(torch.FloatTensor(weights['encoder_norm']['scale']						)						)

   return model







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     nn.Parameter(torch.FloatTensor(weights['time_emb_dense0']['kernel'].T						)						)
   A__       =     nn.Parameter(torch.FloatTensor(weights['time_emb_dense1']['kernel'].T						)						)

   A__       =     nn.Parameter(
       torch.FloatTensor(weights['Embed_0']['embedding']						)      ,requires_grad=UpperCAmelCase						)

   A__       =     nn.Parameter(
       torch.FloatTensor(weights['continuous_inputs_projection']['kernel'].T						)						)

   for lyr_num, lyr in enumerate(model.decoders						):
      A__       =     weights[F"""layers_{lyr_num}"""]
      A__       =     nn.Parameter(
          torch.FloatTensor(ly_weight['pre_self_attention_layer_norm']['scale']						)						)

      A__       =     nn.Parameter(
          torch.FloatTensor(ly_weight['FiLMLayer_0']['DenseGeneral_0']['kernel'].T						)						)

      A__       =     ly_weight['self_attention']
      A__       =     nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T						)						)
      A__       =     nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T						)						)
      A__       =     nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T						)						)
      A__       =     nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T						)						)

      A__       =     ly_weight['MultiHeadDotProductAttention_0']
      A__       =     nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T						)						)
      A__       =     nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T						)						)
      A__       =     nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T						)						)
      A__       =     nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T						)						)
      A__       =     nn.Parameter(
          torch.FloatTensor(ly_weight['pre_cross_attention_layer_norm']['scale']						)						)

      A__       =     nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']						)						)
      A__       =     nn.Parameter(
          torch.FloatTensor(ly_weight['FiLMLayer_1']['DenseGeneral_0']['kernel'].T						)						)
      A__       =     nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T						)						)
      A__       =     nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T						)						)
      A__       =     nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T						)						)

   A__       =     nn.Parameter(torch.FloatTensor(weights['decoder_norm']['scale']						)						)

   A__       =     nn.Parameter(torch.FloatTensor(weights['spec_out_dense']['kernel'].T						)						)

   return model







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     checkpoints.load_tax_checkpoint(args.checkpoint_path						)
   A__       =     jnp.tree_util.tree_map(onp.array      ,UpperCAmelCase						)

   A__       =     [
       'from __gin__ import dynamic_registration',
       'from music_spectrogram_diffusion.models.diffusion import diffusion_utils',
       'diffusion_utils.ClassifierFreeGuidanceConfig.eval_condition_weight = 2.0',
       'diffusion_utils.DiffusionConfig.classifier_free_guidance = @diffusion_utils.ClassifierFreeGuidanceConfig()',
   ]

   A__       =     os.path.join(args.checkpoint_path      ,'..'      ,'config.gin'						)
   A__       =     inference.parse_training_gin_file(UpperCAmelCase      ,UpperCAmelCase						)
   A__       =     inference.InferenceModel(args.checkpoint_path      ,UpperCAmelCase						)

   A__       =     DDPMScheduler(beta_schedule='squaredcos_cap_v2'      ,variance_type='fixed_large'						)

   A__       =     SpectrogramNotesEncoder(
       max_length=synth_model.sequence_length['inputs']      ,vocab_size=synth_model.model.module.config.vocab_size      ,d_model=synth_model.model.module.config.emb_dim      ,dropout_rate=synth_model.model.module.config.dropout_rate      ,num_layers=synth_model.model.module.config.num_encoder_layers      ,num_heads=synth_model.model.module.config.num_heads      ,d_kv=synth_model.model.module.config.head_dim      ,d_ff=synth_model.model.module.config.mlp_dim      ,feed_forward_proj='gated-gelu'      ,)

   A__       =     SpectrogramContEncoder(
       input_dims=synth_model.audio_codec.n_dims      ,targets_context_length=synth_model.sequence_length['targets_context']      ,d_model=synth_model.model.module.config.emb_dim      ,dropout_rate=synth_model.model.module.config.dropout_rate      ,num_layers=synth_model.model.module.config.num_encoder_layers      ,num_heads=synth_model.model.module.config.num_heads      ,d_kv=synth_model.model.module.config.head_dim      ,d_ff=synth_model.model.module.config.mlp_dim      ,feed_forward_proj='gated-gelu'      ,)

   A__       =     TaFilmDecoder(
       input_dims=synth_model.audio_codec.n_dims      ,targets_length=synth_model.sequence_length['targets_context']      ,max_decoder_noise_time=synth_model.model.module.config.max_decoder_noise_time      ,d_model=synth_model.model.module.config.emb_dim      ,num_layers=synth_model.model.module.config.num_decoder_layers      ,num_heads=synth_model.model.module.config.num_heads      ,d_kv=synth_model.model.module.config.head_dim      ,d_ff=synth_model.model.module.config.mlp_dim      ,dropout_rate=synth_model.model.module.config.dropout_rate      ,)

   A__       =     load_notes_encoder(ta_checkpoint['target']['token_encoder']      ,UpperCAmelCase						)
   A__       =     load_continuous_encoder(ta_checkpoint['target']['continuous_encoder']      ,UpperCAmelCase						)
   A__       =     load_decoder(ta_checkpoint['target']['decoder']      ,UpperCAmelCase						)

   A__       =     OnnxRuntimeModel.from_pretrained('kashif/soundstream_mel_decoder'						)

   A__       =     SpectrogramDiffusionPipeline(
       notes_encoder=UpperCAmelCase      ,continuous_encoder=UpperCAmelCase      ,decoder=UpperCAmelCase      ,scheduler=UpperCAmelCase      ,melgan=UpperCAmelCase      ,)
   if args.save:
      pipe.save_pretrained(args.output_path						)


if __name__ == "__main__":
    lowerCAmelCase_     						=				argparse.ArgumentParser()

    parser.add_argument('''--output_path''', default=None, type=str, required=True, help='''Path to the converted model.''')
    parser.add_argument(
        '''--save''', default=True, type=bool, required=False, help='''Whether to save the converted model or not.'''
    )
    parser.add_argument(
        '''--checkpoint_path''',
        default=f'''{MODEL}/checkpoint_500000''',
        type=str,
        required=False,
        help='''Path to the original jax model checkpoint.''',
    )
    lowerCAmelCase_     						=				parser.parse_args()

    main(args)








'''simple docstring'''







def 						_A      ( ):






   '''simple docstring'''







   A__       =     [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]

   A__       =     6
   A__       =     1
   A__       =     1901

   A__       =     0

   while year < 2001:
      day += 7

      if (year % 4 == 0 and year % 100 != 0) or (year % 400 == 0):
         if day > days_per_month[month - 1] and month != 2:
            month += 1
            A__       =     day - days_per_month[month - 2]
         elif day > 29 and month == 2:
            month += 1
            A__       =     day - 29
      else:
         if day > days_per_month[month - 1]:
            month += 1
            A__       =     day - days_per_month[month - 2]

      if month > 12:
         year += 1
         A__       =     1

      if year < 2001 and day == 1:
         sundays += 1
   return sundays


if __name__ == "__main__":
    print(solution())


